Staging Lane to Put-Away, the GEODIS Case Study

GEODIS, a leading global supply chain operator, was proactively looking for a way to enhance the process of picking up, delivering, and putting away materials in distribution centers. In the fall of 2019, the company began researching solutions to make teammates’ jobs easier and safer, all while increasing efficiency and productivity.

As a 3PL, GEODIS handles many different operations within its distribution centers. In order to increase overall productivity, GEODIS determined that the flexibility offered by AMRs would be suitable for operation in their dynamic workflows. After assessing various potential offerings, GEODIS engaged with Vecna Robotics.

Impressed by the Vecna Robotics solution, which aligned to the organization’s needs, GEODIS elected to deploy two units during a pilot in October 2019 at one of its busiest sites in Dallas, TX. The deployment of Vecna Robotics’ pallet jack AMRs helped increase productivity and throughput in GEODIS’ 350,000 square foot retail case pick and consolidation distribution center, establishing a proven use case.

Today, Vecna Robotics’ AMRs clear designated dock doors and bring pallets to pick and drop spots (P&Ds) throughout the facility to increase inbound throughput and dock-to-stock time, working alongside lift drivers. By using the AMRs to eliminate horizontal travel and reduce trips to the dock, put-away operator productivity has improved by approximately 30%.

Vecna Robotics is an autonomous mobile robot company that helps distribution, warehousing, and manufacturing organizations streamline their materials handling and logistics operations. The company is the only solutions provider that offers high capacity AMRs for full-pallet put-away. Additionally, Vecna Robotics’ AMRs leverage the company’s artificial intelligence-powered orchestration engine, Pivotal^tm, which optimizes and organizes daily robot work orders and allocates tasks to the appropriate agent based on priority, location and availability. Vecna Robotics’ software also utilizes machine learning algorithms to track the vehicles and share data-driven insights to create a continuous improvement cycle.